Currently, the use of robots has been expanding in human society including in the home. At present, an intellectual development mechanism for robots is still being developed, and thus actions that can be executed by robots and things that can be understood by robots are limited. On the other hand, various types of robots having different body characteristics have been developed. Assuming that such robots having the above limitations are introduced into the standard home, it would be inefficient to cause each of these robots to learn actions and the like independently.
Accordingly, there is a need for a mechanism for sharing the knowledge learned by each robot, in particular, action information, among other robots. The technique for obtaining an action by sharing action information among robots is referred to as “action transfer”. In other words, the action transfer is a technique in which a transfer destination (target domain) robot efficiently learns an action by using action information obtained by a transfer source (source domain) robot.
Information (action information) based on which a robot acts can be considered as, for example, being information obtained by accumulating correspondence relations between a joint angle (joint value) and coordinates (end effector) of a leading end of an arm of a robot having certain physical properties (e.g., the length of an arm, or the number of joints). The use of such action information enables the robot to act. Accordingly, the physical properties have an important effect on the robot action transfer. However, it is difficult to identify the physical properties of a robot among various types of robots. Therefore, a mechanism for adapting action information obtained from another robot to the physical properties of the robot is important in the action transfer.
As the above mechanism, a technique in which physical properties of a transfer destination robot are obtained and then action information about a transfer destination robot is processed to be adapted to the physical properties of the transfer source robot is generally employed. However, in this technique, some advance preparation such as measurement of physical properties of the transfer destination robot is required. Further, if the physical properties of the transfer destination robot are changed, or if an error occurs in the measurement thereof, it is difficult to perform an action accurately. Furthermore, humans and animals can learn actions without obtaining information about their own physical properties in advance. Considering this, it seems to be a more realistic approach to achieve an action transfer based on experiences of a real robot, without obtaining information about the physical properties of the transfer destination robot in advance.
In this regard, Non Patent Literature 1 proposes a technique for transferring action samples of a transfer source robot to a transfer destination robot by using the respective numbers of action samples acquired from both the transfer source robot and the transfer destination robot that are the same as each other even when some of the physical properties of the transfer destination robot are unknown. Note that in this case, the transfer is achieved by fitting using a matrix calculation.